Sea-ice is an important operational item for real timely monitoring and forecasting marine environment of China. This paper introduces an operational method of satellite remote sensing to monitor sea- ice using quanti...Sea-ice is an important operational item for real timely monitoring and forecasting marine environment of China. This paper introduces an operational method of satellite remote sensing to monitor sea- ice using quantitative data of NOAA, and its contents include computer processing of AVHRR sounding data of NOAA and its program design, imagery processing of sea-ice imagery from satellite and their thematic analysis. The sea-ice satellite colour imageries processed via this software system are able to interpret sea-ice pattern, characterizing it by thickness, maximum position of ice boundary, floe concentration and dynamic process of ice changing. At the same time, analyses of the ice condition of the Bohai Sea for the two-year period (1986-1988) as monitored by satellite have been summarized.展开更多
The environmental conditions in China are still very serious. In the years to come, the mission for environmental treatment and protection, supervision,
There is an urgent need for the development of a method that can undertake rapid, effective, and accurate monitoring and identification of fog by satellite remote sensing, since heavy fog can cause enormous disasters ...There is an urgent need for the development of a method that can undertake rapid, effective, and accurate monitoring and identification of fog by satellite remote sensing, since heavy fog can cause enormous disasters to China’s national economy and people's lives and property in the urban and coastal areas. In this paper, the correlative relationship between the reflectivity of land surface and clouds in different time phases is found, based on the analysis of the radiative and satellite-based spectral characteristics of fog. Through calculation and analyses of the relative variability of the reflectivity in the images, the threshold to identify quasi-fog areas is generated automatically. Furthermore, using the technique of quick image run-length encoding, and in combination with such practical methods as analyzing texture and shape fractures, smoothness, and template characteristics, the automatic identification of fog and fog-cloud separation using meteorological satellite remote sensing images are studied, with good results in application.展开更多
Recently near-ground remote sensing using unmanned aerial vehicles(UAV)witnessed wide applications in obtaining field information.In this research,four Rapideye satellite images and eight RGB images acquired from UAV ...Recently near-ground remote sensing using unmanned aerial vehicles(UAV)witnessed wide applications in obtaining field information.In this research,four Rapideye satellite images and eight RGB images acquired from UAV were used from early June to the end of July,2015 covering two experimental winter wheat fields,in order to monitor wheat canopy growth status and analyze the correlation among satellite images based normalized difference vegetation index(NDVI)with UAV’s RGB images based visible-band difference vegetation index(VDVI)and ground variables of the sampled grain protein contents.Firstly,through image interpretation of UAV’s multi-temporal RGB images with fine spatial resolution,the wheat canopy color changes could be intuitively and clearly monitored.Subsequently,by monitoring the changes of satellite images based NDVI as well as VDVI values and UAV’s RGB images based VDVI values,the conclusions were made that these three vegetation indices demonstrated the same and synchronized trend of increasing at the early stage of wheat growth season,reaching up to peak values at the same timing,and starting to decrease since then.The results of the correlation analysis between NDVI of satellite images and sampled grain protein contents show that NDVI has good predicative capability for mapping grain protein content before ripening growth stage around June7,2015,while the reliability of using satellite image based NDVI to predict grain protein contents becomes worse as ripening stage approaches.The regression analysis between UAV’s RGB image based VDVI and satellite image based VDVI as well as NDVI showed good coefficients of determination.It is concluded that it is feasible and practical to temporally complement satellite remote sensing by using UAV’s RGB images based vegetation indices to monitor wheat growth status and to map within-field spatial variations of grain protein contents for small scale farmlands.展开更多
文摘Sea-ice is an important operational item for real timely monitoring and forecasting marine environment of China. This paper introduces an operational method of satellite remote sensing to monitor sea- ice using quantitative data of NOAA, and its contents include computer processing of AVHRR sounding data of NOAA and its program design, imagery processing of sea-ice imagery from satellite and their thematic analysis. The sea-ice satellite colour imageries processed via this software system are able to interpret sea-ice pattern, characterizing it by thickness, maximum position of ice boundary, floe concentration and dynamic process of ice changing. At the same time, analyses of the ice condition of the Bohai Sea for the two-year period (1986-1988) as monitored by satellite have been summarized.
文摘The environmental conditions in China are still very serious. In the years to come, the mission for environmental treatment and protection, supervision,
基金Key research project "Research of Shanghai City and Costal Heavy Fog Remote Sensing Detecting and Warning System" of Science and Technology Commission of Shanghai Municipality (075115011)
文摘There is an urgent need for the development of a method that can undertake rapid, effective, and accurate monitoring and identification of fog by satellite remote sensing, since heavy fog can cause enormous disasters to China’s national economy and people's lives and property in the urban and coastal areas. In this paper, the correlative relationship between the reflectivity of land surface and clouds in different time phases is found, based on the analysis of the radiative and satellite-based spectral characteristics of fog. Through calculation and analyses of the relative variability of the reflectivity in the images, the threshold to identify quasi-fog areas is generated automatically. Furthermore, using the technique of quick image run-length encoding, and in combination with such practical methods as analyzing texture and shape fractures, smoothness, and template characteristics, the automatic identification of fog and fog-cloud separation using meteorological satellite remote sensing images are studied, with good results in application.
基金supported by the R&D Program of Fundamental Technology and Utilization of Social Big Data by the National Institute of Information and Communications Technology(NICT),Japan.
文摘Recently near-ground remote sensing using unmanned aerial vehicles(UAV)witnessed wide applications in obtaining field information.In this research,four Rapideye satellite images and eight RGB images acquired from UAV were used from early June to the end of July,2015 covering two experimental winter wheat fields,in order to monitor wheat canopy growth status and analyze the correlation among satellite images based normalized difference vegetation index(NDVI)with UAV’s RGB images based visible-band difference vegetation index(VDVI)and ground variables of the sampled grain protein contents.Firstly,through image interpretation of UAV’s multi-temporal RGB images with fine spatial resolution,the wheat canopy color changes could be intuitively and clearly monitored.Subsequently,by monitoring the changes of satellite images based NDVI as well as VDVI values and UAV’s RGB images based VDVI values,the conclusions were made that these three vegetation indices demonstrated the same and synchronized trend of increasing at the early stage of wheat growth season,reaching up to peak values at the same timing,and starting to decrease since then.The results of the correlation analysis between NDVI of satellite images and sampled grain protein contents show that NDVI has good predicative capability for mapping grain protein content before ripening growth stage around June7,2015,while the reliability of using satellite image based NDVI to predict grain protein contents becomes worse as ripening stage approaches.The regression analysis between UAV’s RGB image based VDVI and satellite image based VDVI as well as NDVI showed good coefficients of determination.It is concluded that it is feasible and practical to temporally complement satellite remote sensing by using UAV’s RGB images based vegetation indices to monitor wheat growth status and to map within-field spatial variations of grain protein contents for small scale farmlands.
